MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection

TL;DR

This paper presents a CNN-based approach for oil pipeline defect detection using Magnetic Flux Leakage data, emphasizing preprocessing techniques to improve detection performance and address data limitations in industrial applications.

Contribution

It introduces novel preprocessing methods for MFL data and applies advanced CNN architectures to enhance defect detection accuracy in oil pipelines.

Findings

Effective preprocessing improves CNN detection performance.

Proposed methods achieve high accuracy on real-world data.

Approaches reduce inspection time and costs.

Abstract

Recently, the application of computer vision for anomaly detection has been under attention in several industrial fields. An important example is oil pipeline defect detection. Failure of one oil pipeline can interrupt the operation of the entire transportation system or cause a far-reaching failure. The automated defect detection could significantly decrease the inspection time and the related costs. However, there is a gap in the related literature when it comes to dealing with this task. The existing studies do not sufficiently cover the research of the Magnetic Flux Leakage data and the preprocessing techniques that allow overcoming the limitations set by the available data. This work focuses on alleviating these issues. Moreover, in doing so, we exploited the recent convolutional neural network structures and proposed robust approaches, aiming to acquire high performance considering the related metrics. The proposed approaches and their applicability were verified using real-world data.